Nonlocal Self-SimilaritySparse RepresentationIn the past decade, the sparsity prior of image is investigated and utilized widely as the development of compressed sensing theory. The dictionary learning combined with the convex optimization methods promotes the sparse representation to be one of the state...
nonlocal self-similarity (NSS) prior coupled with adaptive regularization have shown great potential in AWGN removal and led to satisfactory denoising performan... J Jiang,J Yang,Y Cui,... - 《Signal Processing》 被引量: 5发表: 2015年 Image denoising using group sparsity residual and external...
Various priors of natural image, such as gradient based prior, nonlocal self-similarity based prior etc., have been widely studied for noise removal. ... S Jia,L Ying,S Zhou,... 被引量: 1发表: 2015年 Nonlocal Speckle Denoising Model Based on Non-linear Partial Differential Equations Imag...
This is an official PyTorch release of the paper "SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with Nonlocal Self-Similarity" - YoungJooHan/SS-BSN
According to the nonlocal self-similarity property of natural images, group-based simultaneous sparse coding (GSSC) model assumes that nonlocal similar patches have similar sparse representations in a given dictionary and have been widely used in various image inverse problems. Inspired by the success...
摘要: Nonlocal self-similarity shows great potential in 被引量: 4 年份: 2018 收藏 引用 批量引用 报错 分享 全部来源 免费下载 求助全文 ir.ia.ac.cn (全网免费下载) 相似文献 参考文献 引证文献Weighted Nuclear Norm Minimization with Application to Image Denoising Shuhang Gu , Lei Zhang , ...
The global correlation across spectrum (GCS) and nonlocal self-similarity (NSS) over space are two important characteristics for HSI. In this paper, a nonlocal low-rank regularized CANDECOMP/PARAFAC (CP) tensor decomposition (NLR-CPTD) is proposed to fully utilize these two intrins...
We would employ both the fractal image coding and the nonlocal self-similarity priors to achieve image compression in image denoising problems. Specifically, we propose a new image denoising model consisting of three terms: a patch-based nonlocal low-rank prior, a data-fidelity term describing ...
Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global...
Based on this and another heart-stirring property called nonlocal self-similarity, some researchers have developed nonlocal sparse regularization models to unify the local sparsity and the nonlocal self-similarity into a variational framework for image deblurring. In such models, the similarity ...